Latent scene representation plays a significant role in training reinforcement learning (RL) agents. To obtain good latent vectors describing the scenes, recent works incorporate the 3D-aware ...latent-conditioned NeRF pipeline into scene representation learning. However, these NeRF-related methods struggle to perceive 3D structural information due to the inefficient dense sampling in volumetric rendering. Moreover, they lack fine-grained semantic information included in their scene representation vectors because they evenly consider free and occupied spaces. Both of them can destroy the performance of downstream RL tasks. To address the above challenges, we propose a novel framework that adopts the efficient 3D Gaussian Splatting (3DGS) to learn 3D scene representation for the first time. In brief, we present the Query-based Generalizable 3DGS to bridge the 3DGS technique and scene representations with more geometrical awareness than those in NeRFs. Moreover, we present the Hierarchical Semantics Encoding to ground the fine-grained semantic features to 3D Gaussians and further distilled to the scene representation vectors. We conduct extensive experiments on two RL platforms including Maniskill2 and Robomimic across 10 different tasks. The results show that our method outperforms the other 5 baselines by a large margin. We achieve the best success rates on 8 tasks and the second-best on the other two tasks.
Despite the promising performance of existing visual models on public benchmarks, the critical assessment of their robustness for real-world applications remains an ongoing challenge. To bridge this ...gap, we propose an explainable visual dataset, XIMAGENET-12, to evaluate the robustness of visual models. XIMAGENET-12 consists of over 200K images with 15,410 manual semantic annotations. Specifically, we deliberately selected 12 categories from ImageNet, representing objects commonly encountered in practical life. To simulate real-world situations, we incorporated six diverse scenarios, such as overexposure, blurring, and color changes, etc. We further develop a quantitative criterion for robustness assessment, allowing for a nuanced understanding of how visual models perform under varying conditions, notably in relation to the background. We make the XIMAGENET-12 dataset and its corresponding code openly accessible at \url{https://sites.google.com/view/ximagenet-12/home}. We expect the introduction of the XIMAGENET-12 dataset will empower researchers to thoroughly evaluate the robustness of their visual models under challenging conditions.
With the increase in dependence on renewable energy sources, interest in energy storage systems has increased, particularly with solar cells, redox flow batteries, and lithium batteries. Multiple ...diagnostic techniques have been utilized to characterize various factors in relation to the battery performance. Electrochemical tests were used to study the energy density, capacity, cycle life, rate, and other related properties. Furthermore, it is critical to correlate the information collected from the characterization of materials to its properties while functioning for advanced batteries. In situ and operando electron microscopy methods are specifically designed to conduct such characterization, and analysis was found to be the best method to achieve that objective. However, the characterization information collected varies according to the types of electron microscopy techniques. Also, the use of complementary analytical techniques further provides a more comprehensive study of these different characterizations, giving insights into the morphology-performance relationship of battery materials and interfaces. Within this review, the focus is on in situ and operando electron microscopy characterization of battery materials, including transmission electron microscopy (TEM), scanning electron microscopy (SEM), cryogenic transmission electron microscopy (Cryo-TEM), and three-dimensional (3D) electron tomography. This review aims to cover both advanced electron microscopy imaging techniques and their applications in the characterization of battery materials involving cathode, anode, and separator and solid electrolyte interphase (SEI). The review discusses a range of advanced electron microscopy techniques, including TEM, SEM, and atomic force microscopy, as well as associated analytical techniques such as energy-dispersive X-ray spectroscopy and electron energy loss spectroscopy. The use of these techniques has led to significant advances in our understanding of battery materials, including the identification of new phases and structures, the study of interface properties, and the characterization of defects and degradation mechanisms. Future perspectives on these advanced electron microscopy techniques and opportunities are also discussed. Overall, this review highlights the importance of electron microscopy in battery research and the potential for these techniques to drive future advancements in the field.
As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is ...gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this paper, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. Unlike conventional deep neural networks (DNNs), which learn a fine-tuned but possibly outdated or biased prediction model using the entire training dataset with high computational complexity, PA-Cache weighs a large set of content features and trains the multi-layer recurrent neural network from shallow to deeper when more requests arrive over time. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider. \rb{The results show that PA-Cache outperforms existing popular caching algorithms and approximates the optimal algorithm with only a 3.8\% performance gap when the cache percentage is 1.0\%}. PA-Cache also significantly reduces the computational cost compared to conventional DNN-based approaches.
We observe the magnetic field morphology towards a nearby star-forming filamentary cloud, G202.3+2.5, by the JCMT/POL-2 850 {\mu}m thermal dust polarization observation with an angular resolution of ...14.4" (~0.053 pc). The average magnetic field orientation is found to be perpendicular to the filaments while showing different behaviors in the four subregions, suggesting various effects from filaments' collision in these subregions. With the kinematics obtained by N2H+ observation by IRAM, we estimate the plane-of-sky (POS) magnetic field strength by two methods, the classical Davis-Chandrasekhar-Fermi (DCF) method and the angular dispersion function (ADF) method, B_{pos,dcf} and B_{pos,adf} are ~90 {\mu}G and ~53 {\mu}G. We study the relative importance between the gravity (G), magnetic field (B) and turbulence (T) in the four subregions, find G > T > B, G >= T > B, G ~ T > B and T > G > B in the north tail, west trunk, south root and east wing, respectively. In addition, we investigate the projection effect on the DCF and ADF methods based on a similar simulation case and find the 3D magnetic field strength may be underestimated by a factor of ~3 if applying the widely-used statistical B_{pos}-to-B_{3D} factor when using DCF or ADF method, which may further underestimate/overestimate related parameters.
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